0 Bibliographic Collection
Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)
Data format: *.bib
Query: “big data” (All Fields) and “national park”
(All Fields)
Timespan: 2015-2024
Document Type: Articles, letters, review and
proceedings papers
Query data: May, 2024
# Loading data
myfile <- ("data_raw/search_1.bib")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(file=myfile, dbsource="isi",format="bibtex")
##
## Converting your isi collection into a bibliographic dataframe
##
## Done!
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##
## Generating affiliation field tag AU_UN from C1: Done!
1 Descriptive Analysis
Although bibliometrics is mainly known for quantifying the scientific
production and measuring its quality and impact, it is also useful for
displaying and analyzing the intellectual, conceptual and social
structures of research as well as their evolution and dynamical
aspects.
In this way, bibliometrics aims to describe how specific disciplines,
scientific domains, or research fields are structured and how they
evolve over time. In other words, bibliometric methods help to map the
science (so-called science mapping) and are very useful in the case of
research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical
methods, which can be used to analyze scientific big data quantitatively
and their evolution over time and discover information. Network
structure is often used to model the interaction among authors,
papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of
data-analysis and data-visualization. After converting and uploading
bibliographic data in R, Bibliometrix performs a descriptive analysis
and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual
research development, the top “k” productive authors, papers, countries
and most relevant keywords.
1.1 Main findings
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
MAIN INFORMATION ABOUT DATA
Timespan 2015 : 2024
Sources (Journals, Books, etc) 63
Documents 82
Annual Growth Rate % 22.03
Document Average Age 3.04
Average citations per doc 23.6
Average citations per year per doc 4.194
References 5155
DOCUMENT TYPES
article 71
article; early access 3
article; retracted publication 1
editorial material 1
proceedings paper 4
review 2
DOCUMENT CONTENTS
Keywords Plus (ID) 385
Author's Keywords (DE) 432
AUTHORS
Authors 625
Author Appearances 671
Authors of single-authored docs 1
AUTHORS COLLABORATION
Single-authored docs 1
Documents per Author 0.131
Co-Authors per Doc 8.18
International co-authorships % 32.93
Annual Scientific Production
Year Articles
2015 1
2016 4
2017 4
2018 4
2019 7
2020 9
2021 13
2022 12
2023 22
2024 6
Annual Percentage Growth Rate 22.03
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 HE H 4 CIESIELSKI M 1.000
2 ZHENG H 4 ZOU SS 1.000
3 DELANG CO 3 LI W 0.833
4 HEURICH M 3 LI Y 0.667
5 LI 3 ZHANG X 0.643
6 LI X 3 HE H 0.601
7 LI Y 3 ZHENG H 0.601
8 LU J 3 CHAPMAN CA 0.500
9 WU Y 3 CHEN J 0.500
10 ZHANG X 3 HALPENNY E 0.500
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 MCKINLEY DC, 2017, BIOL CONSERV 10.1016/j.biocon.2016.05.015 583 72.88 2.740
2 HEIKINHEIMO V, 2017, ISPRS INT GEO-INF 10.3390/ijgi6030085 178 22.25 0.837
3 SWANSON A, 2016, CONSERV BIOL 10.1111/cobi.12695 157 17.44 3.323
4 RICH LN, 2017, GLOB ECOL BIOGEOGR 10.1111/geb.12600 88 11.00 0.414
5 MANCINI F, 2018, PLOS ONE 10.1371/journal.pone.0200565 69 9.86 2.173
6 GATTI RC, 2022, PROC NATL ACAD SCI U S A 10.1073/pnas.2115329119 66 22.00 5.462
7 SHASHA ZT, 2020, ENVIRON SCI POLLUT RES 10.1007/s11356-020-08584-9 54 10.80 1.984
8 LI X, 2019, SCI BULL 10.1016/j.scib.2019.07.004 53 8.83 3.373
9 LASTOVICKA J, 2020, REMOTE SENS 10.3390/rs12121914 47 9.40 1.727
10 BARROS C, 2020, CURR ISSUES TOUR 10.1080/13683500.2019.1619674 47 9.40 1.727
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 CHINA 32 0.3902 26 6 0.188
2 USA 20 0.2439 14 6 0.300
3 GERMANY 4 0.0488 1 3 0.750
4 UNITED KINGDOM 4 0.0488 2 2 0.500
5 SPAIN 3 0.0366 3 0 0.000
6 AUSTRALIA 2 0.0244 1 1 0.500
7 CANADA 2 0.0244 1 1 0.500
8 FINLAND 2 0.0244 1 1 0.500
9 ITALY 2 0.0244 0 2 1.000
10 JAPAN 2 0.0244 0 2 1.000
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 USA 953 47.65
2 UNITED KINGDOM 250 62.50
3 CHINA 245 7.66
4 FINLAND 185 92.50
5 GERMANY 57 14.25
6 SPAIN 51 17.00
7 CZECH REPUBLIC 47 47.00
8 CANADA 38 19.00
9 KOREA 32 32.00
10 SWITZERLAND 22 22.00
Most Relevant Sources
Sources Articles
1 SUSTAINABILITY 6
2 ECOLOGICAL INDICATORS 3
3 FORESTS 3
4 GLOBAL ECOLOGY AND CONSERVATION 3
5 REMOTE SENSING 3
6 CONSERVATION BIOLOGY 2
7 CURRENT ISSUES IN TOURISM 2
8 HELIYON 2
9 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2
10 JOURNAL OF DESTINATION MARKETING \\& MANAGEMENT 2
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 BIG DATA 11 NATIONAL-PARK 13
2 NATIONAL PARK 6 PATTERNS 11
3 PROTECTED AREA 5 BIG DATA 9
4 ECOSYSTEM SERVICES 4 CONSERVATION 8
5 MACHINE LEARNING 4 IMPACT 7
6 NATIONAL PARKS 4 BIODIVERSITY 6
7 TIME SERIES 4 MANAGEMENT 6
8 TOURISM 4 TOURISM 6
9 ARTIFICIAL INTELLIGENCE 3 FRAMEWORK 5
10 CLIMATE CHANGE 3 VISITATION 5
plot(x=results, k=10, pause=F)
Warning: Removed 1 rows containing non-finite values (`stat_align()`).
1.2 Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
WOOD SA, 2013, SCI REP-UK, V3, DOI 10.1038/SREP02976 8
HAUSMANN A, 2018, CONSERV LETT, V11, DOI 10.1111/CONL.12343 7
HEIKINHEIMO V, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/IJGI6030085 7
SESSIONS C, 2016, J ENVIRON MANAGE, V183, P703, DOI 10.1016/J.JENVMAN.2016.09.018 7
TENKANEN H, 2017, SCI REP-UK, V7, DOI 10.1038/S41598-017-18007-4 7
BALMFORD A, 2009, PLOS BIOL, V7, DOI 10.1371/JOURNAL.PBIO.1000144 6
BLEI DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/JMLR.2003.3.4-5.993 6
BALMFORD A, 2015, PLOS BIOL, V13, DOI 10.1371/JOURNAL.PBIO.1002074 5
GUO Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/J.TOURMAN.2016.09.009 5
HEIKINHEIMO V, 2020, LANDSCAPE URBAN PLAN, V201, DOI 10.1016/J.LANDURBPLAN.2020.103845 4
KEELER BL, 2015, FRONT ECOL ENVIRON, V13, P76, DOI 10.1890/140124 4
PARACCHINI ML, 2014, ECOL INDIC, V45, P371, DOI 10.1016/J.ECOLIND.2014.04.018 4
SONTER LJ, 2016, PLOS ONE, V11, DOI 10.1371/JOURNAL.PONE.0162372 4
STEFFAN-DEWENTER I., 2002, ECOLOGY, V83, P1421, DOI 10.1890/0012-9658(2002)0831421:SDEOLC2.0.CO 4
TOIVONEN T, 2019, BIOL CONSERV, V233, P298, DOI 10.1016/J.BIOCON.2019.01.023 4
WATSON JEM, 2014, NATURE, V515, P67, DOI 10.1038/NATURE13947 4
ZHU Z, 2014, REMOTE SENS ENVIRON, V144, P152, DOI 10.1016/J.RSE.2014.01.011 4
BROWN G, 2011, LANDSCAPE URBAN PLAN, V102, P1, DOI 10.1016/J.LANDURBPLAN.2011.03.003 3
BURTON AC, 2015, J APPL ECOL, V52, P675, DOI 10.1111/1365-2664.12432 3
CESSFORD GORDON, 2003, JOURNAL FOR NATURE CONSERVATION (JENA), V11, P240, DOI 10.1078/1617-1381-00055 3
2 The Intellectual Structure of the field
Citation analysis is one of the main classic techniques in
bibliometrics. It shows the structure of a specific field through the
linkages between nodes (e.g. authors, papers, or journals), while the
edges can be differently interpreted depending on the network type, that
are namely co-citation, direct citation, bibliographic coupling. Please
see Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between
cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of
analysis.
The useful dimensions to comment the co-citation networks are: (i)
centrality and peripherality of nodes, (ii) their proximity and
distance, (iii) strength of ties, (iv) clusters, (iiv) bridging
contributions.
Third, a historiograph is built on direct citations. It draws the
intellectual linkages in a historical order. Cited works of thousands of
authors contained in a collection of published scientific articles is
sufficient for recostructing the historiographic structure of the field,
calling out the basic works in it.
2.1 Article (reference) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 1 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
edges.min = 5 (plots only edges with a strength greater than or
equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Article co-citation network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 5123
Density 0.017
Transitivity 0.928
Diameter 9
Degree Centralization 0.08
Average path length 3.955
Journal (source) co-citation analysis
M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=10, remove.multiple=FALSE, labelsize=0.8,edgesize = 3, edges.min=5)
Descriptive analysis of Journal co-citation network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 2328
Density 0.037
Transitivity 0.397
Diameter 4
Degree Centralization 0.456
Average path length 2.169
3 Historiograph - Direct citation linkages
histResults <- histNetwork(M, sep = ";")
##
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
##
## Analyzing 5428 reference items...
##
## Found 4 documents with no empty Local Citations (LCS)
options(width = 130)
net <- histPlot(histResults, n=20, size = 5, labelsize = 4)
Legend
Label
1 MANCINI F, 2018, PLOS ONE DOI 10.1371/JOURNAL.PONE.0200565
2 CHUN J, 2020, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2020.104136
3 BARROS C, 2020, CURR ISSUES TOUR DOI 10.1080/13683500.2019.1619674
4 CIESIELSKI M, 2021, FOREST POLICY ECON DOI 10.1016/J.FORPOL.2021.102509
5 LU J, 2023, TOUR MANAG PERSPECT DOI 10.1016/J.TMP.2023.101143
6 CIESIELSKI M, 2023, J MT SCI DOI 10.1007/S11629-023-8914-3
7 NYELELE C, 2023, ECOL INDIC DOI 10.1016/J.ECOLIND.2023.110638
8 MIZUUCHI Y, 2024, J FOR RES DOI 10.1080/13416979.2023.2257456
Author_Keywords
1 <NA>
2 NATURE-BASED TOURISM; TOURISM PRESSURE; PROTECTED AREA; SOCIAL BIG DATA; MANAGEMENT POLICY
3 SOCIAL MEDIA DATA; GEOTAGGED PHOTOGRAPHS; GPS TRACKS; NATURE-BASED; TOURISM; NATIONAL PARKS; VISITORS' BEHAVIOUR
4 FOREST RECREATIONAL FUNCTION; FLICKR; PUBLIC PREFERENCES; SOCIAL MEDIA; GEOTAGGED PHOTOS; BIG DATA
5 NATIONAL PARKS; MOBILE DEVICE LOCATION DATA; SOCIAL INEQUITY; DISTANCE; DECAY
6 ECOSYSTEM SERVICES; BIG DATA; TRAFFIC RESEARCH; MONITORING; FORESTS
7 LAKE TAHOE; VALUATION; ECOSYSTEM SERVICES; FLICKR; FOREST; TRAVEL COST
8 LANDSCAPE PERCEPTION; LANDSCAPE PREFERENCE; NATIONAL PARK; GIS; GEOTAGGED VISITOR EMPLOYED PHOTOGRAPHY
KeywordsPlus
1 CULTURAL ECOSYSTEM SERVICES; TOURISM; DEMAND; ECOTOURISM; DESTINATION; VISITATION; LANDSCAPES; PATTERNS
2 PUBLIC-PARTICIPATION GIS; HOME-RANGE; BIODIVERSITY; VALUES; MEDIA; VISITATION; LANDSCAPE; AREAS; LAND
3 CULTURAL ECOSYSTEM SERVICES; GEOGRAPHIC INFORMATION; MOVEMENT PATTERNS; PROTECTED AREAS; BIG DATA; TOURISM; VISITATION; RECREATION; ECOTOURISM; FRAMEWORK
4 CULTURAL ECOSYSTEM SERVICES; GEOGRAPHIC INFORMATION DATA; SOCIAL MEDIA; DATA; PUBLIC PREFERENCES; NATIONAL-PARK; GEOTAGGED PHOTOGRAPHS; DATA-COLLECTION; CHOICE; RECREATION; PATTERNS
5 REGRESSION-MODELS; TRANSPORTATION; ACCESSIBILITY; TOURISM; IMPACT; RATES
6 OUTDOOR RECREATION; NATIONAL-PARK; GREEN SPACE; URBAN; PARTICIPATION; PERCEPTIONS; MANAGEMENT; VISITORS; HOTSPOTS; AREAS
7 CULTURAL ECOSYSTEM SERVICES; NATIONAL-PARK VISITATION; OPPORTUNITY COST; VALUING NATURE; DEMAND; VALUATION; VISITORS; TIME; PHOTOGRAPHS; WILDFIRE
8 AGRARIAN LANDSCAPES; VISUAL PREFERENCES; SPATIAL-ANALYSIS; SOCIAL MEDIA; PERCEPTION; GIS; PHOTOGRAPHS; DIMENSIONS; PREDICTORS; ATTRIBUTES
DOI Year LCS GCS
1 10.1371/journal.pone.0200565 2018 1 69
2 10.1016/j.tourman.2020.104136 2020 1 32
3 10.1080/13683500.2019.1619674 2020 1 47
4 10.1016/j.forpol.2021.102509 2021 2 18
5 10.1016/j.tmp.2023.101143 2023 0 4
6 10.1007/s11629-023-8914-3 2023 0 0
7 10.1016/j.ecolind.2023.110638 2023 0 3
8 10.1080/13416979.2023.2257456 2024 0 0
4 The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links
between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered
by scholars (so-called research front) and identify what are the most
important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the
conceptual structures is useful to analyze the evolution of topics over
time.
Bibliometrix is able to analyze keywords, but also the terms in the
articles’ titles and abstracts. It does it using network analysis or
correspondance analysis (CA) or multiple correspondance analysis (MCA).
CA and MCA visualise the conceptual structure in a two-dimensional
plot.
4.1 Co-word Analysis through Keyword co-occurrences
Plot options:
normalize = “association” (the vertex similarities are normalized
using association strength)
n = 50 (the function plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of the vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 3 (defines the max size of vertex labels)
label.cex = TRUE (The vertex label sizes are proportional to
their degree)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
label.n = 30 (Labels are plotted only for the main 30
vertices)
edges.min = 25 (plots only edges with a strength greater than or
equal to 2)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=15, remove.multiple=F, edgesize = 10, labelsize=2,label.cex=TRUE,label.n=30,edges.min=2)
Descriptive analysis of keyword co-occurrences network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 385
Density 0.028
Transitivity 0.467
Diameter 6
Degree Centralization 0.194
Average path length 3.024
5 Thematic Map
Co-word analysis draws clusters of keywords. They are considered as
themes, whose density and centrality can be used in classifying themes
and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes
according to the quadrant in which they are placed: (1) upper-right
quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3)
lower-left quadrant: emerging or disappearing themes; (4) upper-left
quadrant: very specialized/niche themes.
Please see:
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano,
M. (2022). Thematic Analysis as a New Culturomic Tool: The
Social Media Coverage on COVID-19 Pandemic in Italy.
Sustainability, 14(6), 3643, (https://doi.org/10.3390/su14063643).
Aria M., Misuraca M., Spano M. (2020) Mapping the evolution
of social research and data science on 30 years of Social Indicators
Research, Social Indicators Research. (DOI: )https://doi.org/10.1007/s11205-020-02281-3)
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera,
F. (2011). An approach for detecting, quantifying, and
visualizing the evolution of a research field: A practical application
to the fuzzy sets theory field. Journal of
Informetrics, 5(1), 146-166.
Map=thematicMap(M, field = "ID", n = 250, minfreq = 4,
stemming = FALSE, size = 0.7, n.labels=5, repel = TRUE)
plot(Map$map)
Cluster description
Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 38 × 9
## # Groups: Cluster_Label [13]
## Occurrences Words Cluster Color Cluster_Label Cluster_Frequency btw_centrality clos_centrality pagerank_centrality
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 3 dynamics 1 #E41A1C80 dynamics 13 1262. 0.00144 0.00823
## 2 2 policy 1 #E41A1C80 dynamics 13 375. 0.00163 0.00579
## 3 2 quality 1 #E41A1C80 dynamics 13 295. 0.00154 0.00422
## 4 2 scale 1 #E41A1C80 dynamics 13 230. 0.00159 0.00479
## 5 2 system 1 #E41A1C80 dynamics 13 195. 0.00144 0.00394
## 6 2 urbanization 1 #E41A1C80 dynamics 13 263. 0.00147 0.00602
## 7 11 patterns 2 #377EB880 patterns 89 4354. 0.00190 0.0232
## 8 9 big data 2 #377EB880 patterns 89 3641. 0.00206 0.0213
## 9 8 conservation 2 #377EB880 patterns 89 1989. 0.00181 0.0167
## 10 6 biodiversity 2 #377EB880 patterns 89 2336. 0.00190 0.0137
## # … with 28 more rows
6 The social structure - Collaboration Analysis
Collaboration networks show how authors, institutions
(e.g. universities or departments) and countries relate to others in a
specific field of research. For example, the first figure below is a
co-author network. It discovers regular study groups, hidden groups of
scholars, and pivotal authors. The second figure is called “Edu
collaboration network” and uncovers relevant institutions in a specific
research field and their relations.
6.1 Author collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=1)
Descriptive analysis of author collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 625
Density 0.087
Transitivity 0.999
Diameter 8
Degree Centralization 0.203
Average path length 1.593
6.2 Edu collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Edu collaboration",type = "auto", size=4,size.cex=F,edgesize = 3,labelsize=1)
Descriptive analysis of edu collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 340
Density 0.136
Transitivity 0.982
Diameter 8
Degree Centralization 0.306
Average path length 2.508
6.3 Country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country collaboration",type = "circle", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")
Descriptive analysis of country collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 57
Density 0.61
Transitivity 0.946
Diameter 3
Degree Centralization 0.283
Average path length 1.408
